In this section, we present results on MNIST [1]}, Fashion MNIST [2]}, and SVHN [3]} datasets. The MNIST and Fashion MNIST databases have a total of 60k training and 10k testing \(28\times 28\)  grey-scale images with ten different classes. SVHN consists of \(32\times 32\)  RGB images with a total of 73257 training images and 26032 testing images with ten different classes. We do not apply any data augmentation method in MNIST and Fashion MNIST datasets. We have applied standard data augmentation methods like rotation, zoom, height shift, shearing on the SVHN dataset. We report results with VGG-16 [4]} (with batch-normalization) network in Table REF .
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